Single-instruction multiple-data (SIMD) processors are characterized by having an array of processors that perform the same operation simultaneously on every element of a data array. Vector processing, an application of SIMD processors, uses vector instructions, which specify the operation to be performed and specify the list of operands, i.e., the data vector, on which it will operate.
The use of processor arrays and vector processing can result in extensive parallelism, resulting in high execution speeds. Yet, despite impressive execution speeds, getting data in and out of the processor can be a problem. Execution speeds are less useful if input/output speeds cannot keep up.
In many applications, such as video processing, real-time processing speed is desirable. Yet, a stumbling block to real-time processing is the large amount of data that must be processed to generate the pixels, lines, and frames of a video picture.
A need exists for an easily manufactured SIMD processor that maximizes data input rates without increasing manufacturing costs. Although the need for such processors is not limited to television, digital television processing involves processing tasks, such as various filtering processes, for which a processor with a fast throughput is desirable. For example, digital comb filtering is used to separate the luminance and chrominance signals from each other. In general, digital filters are expressed as z-transform functions, in which the terms represent weighted time delays.
A problem with existing digital filtering techniques is that calculations are performed with serial processing algorithms and devices, sample-by-sample and tap-by-tap. Yet, newer filter applications require more processing power than is available with these techniques. Some approaches to digital filtering have improved processing speed with custom designed circuits, but this approach sacrifices programming flexibility. As a result, system development is slow and unsophisticated. A need exists for a digital filter that not only achieves a fast throughput, but is also easily adapted to different filter algorithms.